Another case emerges when external conditions that are not relevant for the experiment induce a difference in the observed data's labels in the sense that \(\mathcal {L}(Y|X,A) \ne \mathcal {L}(Y|X)\) , therefore inadvertently encouraging models to learn biased decisions (as in the Wolves versus Huskies example in [1]}). This is the case when data is collected with labels influenced by a third unknown variable leading to confounding bias, or when the observation setting favors one class over the other leading to selection bias. The sources of this bias may be related to observation tools, methods or external factors as it will be pointed out later.
